Overview

Now that the 2020 election is officially over and Biden was elected as the President of the United States, it is important that I reflect on my prediction model. I am excited to see how I cold learn from my model for future models that I create.

Model Recap and Predictions

Let’s first recap on my prediction model to get a better picture of what it was.

Patterns and Accuracy

Overall, I am pretty satisfied with how my model turned out. While I did miss a few states and this is my first election forecast, I was quite happy that I predicted some battleground states correctly.

Above is a comparison between my predictions and the actual results of the 2020 election. As you can see, the states that I got wrong were battleground states. However, I would like to say that the predictive intervals for the battleground states did capture the true result.

Moreover, let’s take a look into the plot above, which plots the actual two-party vote share for Trump against my predictions for Trump. The blue points represent states Biden won and the red points represent states Trump won.

Furthermore, the map above shows the difference between Trump’s actual and predicted two party vote share in each state. A negative difference means that Trump was overpredicted for that particular state while a positive difference means that Trump was underpredicted for that particular state.

Hypotheses for why my model was inaccurate

Now that we have went over my prediction model, it is important to look at possible hypotheses for the inaccuracies seen in my model. My model seemed to incorrectly predict the results for battleground states in particular and it is important we pay attention to the reasons why. Below are my hypotheses for explaining the inaccuracies of my model:

Proposed tests to test hypotheses